Evaluation of the GOES-R ABI LAP Retrieval Algorithm Using the GOES-13 Sounder

2014 ◽  
Vol 31 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Yong-Keun Lee ◽  
Zhenglong Li ◽  
Jun Li ◽  
Timothy J. Schmit
Keyword(s):  
Microwave Radiometer ◽  
Precipitable Water ◽  
Retrieval Algorithm ◽  
Earth Observing System ◽  
Radiosonde Observation ◽  
Global Positioning ◽  
Atmospheric Radiation Measurement ◽  
Atmospheric Profile ◽  
Infrared Radiances ◽  
Environmental Prediction

Abstract A physical retrieval algorithm has been developed for deriving the legacy atmospheric profile (LAP) product from infrared radiances of the Advanced Baseline Imager (ABI) on board the next-generation Geostationary Operational Environmental Satellite (GOES-R) series. In this study, the GOES-R ABI LAP retrieval algorithm is applied to the GOES-13 sounder radiance measurements (termed the GOES-13 LAP retrieval algorithm in this study) for its validation as well as for potential transition of the GOES-13 LAP retrieval algorithm for the operational processing of GOES sounder data. The GOES-13 LAP retrievals are compared with five different truth measurements: radiosonde observation (raob) and microwave radiometer–measured total precipitable water (TPW) at the Atmospheric Radiation Measurement Cloud and Radiation Testbed site, conventional raob, TPW measurements from the global positioning system–integrated precipitable water NOAA network, and TPW measurements from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The results show that with the GOES-R ABI LAP retrieval algorithm, the GOES-13 sounder provides better water vapor profiles than the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecast fields at the levels between 300 and 700 hPa. The root-mean-square error (RMSE) and standard deviation (STD) of the GOES-13 sounder TPW are consistently reduced from those of the GFS forecast no matter which measurements are used as the truth. These substantial improvements indicate that the GOES-R ABI LAP retrieval algorithm is well prepared to provide continuity of quality to some of the current GOES sounder products, and the algorithm can be transferred to process the current GOES sounder measurements for operational product generation.

scholarly journals Analysis of Radiosonde and Ground-Based Remotely Sensed PWV Data from the 2004 North Slope of Alaska Arctic Winter Radiometric Experiment

2007 ◽  
Vol 24 (3) ◽  
pp. 415-431 ◽  
Author(s):  
V. Mattioli ◽  
E. R. Westwater ◽  
D. Cimini ◽  
J. C. Liljegren ◽  
B. M. Lesht ◽  
...  
Keyword(s):  
Microwave Radiometer ◽  
Precipitable Water ◽  
North Slope ◽  
Remote Sensors ◽  
The North ◽  
Global Positioning ◽  
North Slope Of Alaska ◽  
Atmospheric Radiation Measurement ◽  
Sensor Temperature ◽  
Better Than

Abstract During 9 March–9 April 2004, the North Slope of Alaska Arctic Winter Radiometric Experiment was conducted at the Atmospheric Radiation Measurement Program’s (ARM) “Great White” field site near Barrow, Alaska. The major goals of the experiment were to compare microwave and millimeter wavelength radiometers and to develop forward models in radiative transfer, all with a focus on cold (temperature from 0° to −40°C) and dry [precipitable water vapor (PWV) < 0.5 cm] conditions. To supplement the remote sensors, several radiosonde packages were deployed: Vaisala RS90 launched at the ARM Duplex and at the Great White and Sippican VIZ-B2 operated by the NWS. In addition, eight dual-radiosonde launches were conducted at the Duplex with Vaisala RS90 and Sippican GPS Mark II, the latter one modified to include a chilled mirror humidity sensor. Temperature comparisons showed a nighttime bias between VIZ-B2 and RS90, which reached 3.5°C at 30 hPa. Relative humidity comparisons indicated better than 5% average agreement between the RS90 and the chilled mirror. A bias of about 20% for the upper troposphere was found in the VIZ-B2 and the Mark II measurements relative to both RS90 and the chilled mirror. Comparisons in PWV were made between a microwave radiometer, a microwave profiler, a global positioning system receiver, and the radiosonde types. An RMS agreement of 0.033 cm was found between the radiometer and the profiler and better than 0.058 cm between the radiometers and GPS. RS90 showed a daytime dry bias on PWV of about 0.02 cm.

scholarly journals Carbon monoxide mixing ratios over Oklahoma between 2002 and 2009 retrieved from Atmospheric Emitted Radiance Interferometer spectra

2010 ◽  
Vol 3 (5) ◽  
pp. 1319-1331 ◽  
Author(s):  
L. Yurganov ◽  
W. McMillan ◽  
C. Wilson ◽  
M. Fischer ◽  
S. Biraud ◽  
...  
Keyword(s):  
Water Vapor ◽  
Great Plains ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Retrieval Algorithm ◽  
Southern Great Plains ◽  
Diurnal Cycles ◽  
Mixing Ratios ◽  
Clear Sky ◽  
The Tropics

Abstract. CO mixing ratios for the lowermost 2-km atmospheric layer were retrieved from downwelling infrared (IR) radiance spectra of the clear sky measured between 2002 and 2009 by a zenith-viewing Atmospheric Emitted Radiance Interferometer (AERI) deployed at the Southern Great Plains (SGP) observatory of the Atmospheric Radiation Measurements (ARM) Program near Lamont, Oklahoma. A version of a published earlier retrieval algorithm was improved and validated. Archived temperature and water vapor profiles retrieved from the same AERI spectra through automated ARM processing were used as input data for the CO retrievals. We found the archived water vapor profiles required additional constraint using SGP Microwave Radiometer retrievals of total precipitable water vapor. A correction for scattered solar light was developed as well. The retrieved CO was validated using simultaneous independently measured CO profiles from an aircraft. These tropospheric CO profiles were measured from the surface to altitudes of 4572 m a.s.l. once or twice a week between March 2006 and December 2008. The aircraft measurements were supplemented with ground-based CO measurements using a non-dispersive infrared gas correlation instrument at the SGP and retrievals from the Atmospheric IR Sounder (AIRS) above 5 km to create full tropospheric CO profiles. Comparison of the profiles convolved with averaging kernels to the AERI CO retrievals found a squared correlation coefficient of 0.57, a standard deviation of ±11.7 ppbv, a bias of -16 ppbv, and a slope of 0.92. Averaged seasonal and diurnal cycles measured by the AERI are compared with those measured continuously in situ at the SGP in the boundary layer. Monthly mean CO values measured by the AERI between 2002 and 2009 are compared with those measured by the AIRS over North America, the Northern Hemisphere mid-latitudes, and over the tropics.

Microwave and Millimeter-Wave Radiometric and Radiosonde Observations in an Arctic Environment

2008 ◽  
Vol 25 (10) ◽  
pp. 1768-1777 ◽  
Author(s):  
V. Mattioli ◽  
E. R. Westwater ◽  
D. Cimini ◽  
A. J. Gasiewski ◽  
M. Klein ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
The Arctic ◽  
Atmospheric Radiation ◽  
Stratospheric Water Vapor ◽  
Significant Difference ◽  
Atmospheric Radiation Measurement ◽  
Microwave Radiometers ◽  
The U.S

Abstract In a recent paper by Mattioli et al., a significant difference was observed between upper-tropospheric and lower-stratospheric water vapor profiles as observed by two radiosonde systems operating in the Arctic. The first was the Vaisala RS90 system as operated by the U.S. Department of Energy’s Atmospheric Radiation Measurement Program; the second was the operational radiosondes launched by the U.S. National Weather Service that used the Sippican VIZ-B2 type. Observations of precipitable water vapor by ground-based microwave radiometers and GPS did not reveal these differences. However, both the microwave radiometer profiler (MWRP) and the ground-based scanning radiometer (GSR) contain channels that receive a significant response from the upper-tropospheric region. In this paper, it is shown that brightness temperature (Tb) observations from these instruments are in consistent agreement with calculations based on the RS90 data but differ by several degrees with calculations based on the VIZ radiosondes. It is also shown that calculations of Tb can serve as a gross quality control of upper-tropospheric soundings.

Improved Daytime Column-Integrated Precipitable Water Vapor from Vaisala Radiosonde Humidity Sensors

2008 ◽  
Vol 25 (6) ◽  
pp. 873-883 ◽  
Author(s):  
K. E. Cady-Pereira ◽  
M. W. Shephard ◽  
D. D. Turner ◽  
E. J. Mlawer ◽  
S. A. Clough ◽  
...  
Keyword(s):  
Water Vapor ◽  
Great Plains ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Atmospheric Radiation Measurement ◽  
Atmospheric Transmittance ◽  
Dry Bias

Abstract Accurate water vapor profiles from radiosondes are essential for long-term climate prediction, weather prediction, validation of remote sensing retrievals, and other applications. The Vaisala RS80, RS90, and RS92 radiosondes are among the more commonly deployed radiosondes in the world. However, numerous investigators have shown that the daytime water vapor profiles measured by these instruments present a significant dry bias due to the solar heating of the humidity sensor. This bias in the column-integrated precipitable water vapor (PWV), along with variability due to calibration, can be removed by scaling the humidity profile to agree with the PWV retrieved from a microwave radiometer (MWR), as has been demonstrated by several previous studies. Infrared radiative closure analyses have shown that the MWR PWV does not present daytime versus nighttime differences; thus, scaling by the MWR is a possible approach for removing the daytime dry bias. However, MWR measurements are not routinely available at all radiosonde launch sites. Starting from a long-term series of sonde and MWR PWV measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, the authors have developed a simple correction to the column-integrated sonde PWV, derived from an analysis of the ratio of the MWR and sonde measurements; this correction is a function of the atmospheric transmittance as determined by the solar zenith angle, and it effectively removes the daytime dry bias at all solar zenith angles. The correction was validated by successfully applying it to an independent dataset from the ARM tropical western Pacific (TWP) site.

scholarly journals Observational Constraint for Precipitation in Extratropical Cyclones: Sensitivity to Data Sources

2018 ◽  
Vol 57 (4) ◽  
pp. 991-1009 ◽  
Author(s):  
Catherine M. Naud ◽  
James F. Booth ◽  
Matthew Lebsock ◽  
Mircea Grecu
Keyword(s):  
Microwave Radiometer ◽  
Precipitable Water ◽  
Retrieval Algorithm ◽  
Extratropical Cyclones ◽  
Precipitation Frequency ◽  
Categorical Analysis ◽  
Ground Clutter ◽  
Precipitation Measurement ◽  
Observational Uncertainty ◽  
Global Precipitation

AbstractUsing cyclone-centered compositing and a database of extratropical-cyclone locations, the distribution of precipitation frequency and rate in oceanic extratropical cyclones is analyzed using satellite-derived datasets. The distribution of precipitation rates retrieved using two new datasets, the Global Precipitation Measurement radar–microwave radiometer combined product (GPM-CMB) and the Integrated Multisatellite Retrievals for GPM product (IMERG), is compared with CloudSat, and the differences are discussed. For reference, the composites of AMSR-E, GPCP, and two reanalyses are also examined. Cyclone-centered precipitation rates are found to be the largest with the IMERG and CloudSat datasets and lowest with GPM-CMB. A series of tests is conducted to determine the roles of swath width, swath location, sampling frequency, season, and epoch. In all cases, these effects are less than ~0.14 mm h−1 at 50-km resolution. Larger differences in the composites are related to retrieval biases, such as ground-clutter contamination in GPM-CMB and radar saturation in CloudSat. Overall the IMERG product reports precipitation more often, with larger precipitation rates at the center of the cyclones, in conditions of high precipitable water (PW). The CloudSat product tends to report more precipitation in conditions of dry or moderate PW. The GPM-CMB product tends to systematically report lower precipitation rates than the other two datasets. This intercomparison provides 1) modelers with an observational uncertainty and range (0.21–0.36 mm h−1 near the cyclone centers) when using composites of precipitation for model evaluation and 2) retrieval-algorithm developers with a categorical analysis of the sensitivity of the products to PW.

Separating Cloud and Drizzle Radar Moments during Precipitation Onset Using Doppler Spectra

2013 ◽  
Vol 30 (8) ◽  
pp. 1656-1671 ◽  
Author(s):  
Edward P. Luke ◽  
Pavlos Kollias
Keyword(s):  
Cloud Base ◽  
Doppler Spectrum ◽  
Retrieval Algorithm ◽  
Cloud Droplets ◽  
Gaussian Shape ◽  
Model Parameterization ◽  
Atmospheric Radiation Measurement ◽  
Shape Property ◽  
A New Technique ◽  
Turbulence Parameters

Abstract The retrieval of cloud, drizzle, and turbulence parameters using radar Doppler spectra is challenged by the convolution of microphysical and dynamical influences and the overall uncertainty introduced by turbulence. A new technique that utilizes recorded radar Doppler spectra from profiling cloud radars is presented here. The technique applies to areas in clouds where drizzle is initially produced by the autoconversion process and is detected by a positive skewness in the radar Doppler spectrum. Using the Gaussian-shape property of cloud Doppler spectra, the cloud-only radar Doppler spectrum is estimated and used to separate the cloud and drizzle contributions. Once separated, the cloud spectral peak can be used to retrieve vertical air motion and eddy dissipation rates, while the drizzle peak can be used to estimate the three radar moments of the drizzle particle size distribution. The technique works for nearly 50% of spectra found near cloud top, with efficacy diminishing to roughly 15% of spectra near cloud base. The approach has been tested on a large dataset collected in the Azores during the Atmospheric Radiation Measurement Program (ARM) Mobile Facility deployment on Graciosa Island from May 2009 through December 2010. Validation of the proposed technique is achieved using the cloud base as a natural boundary between radar Doppler spectra with and without cloud droplets. The retrieval algorithm has the potential to characterize the dynamical and microphysical conditions at cloud scale during the transition from cloud to precipitation. This has significant implications for improving the understanding of drizzle onset in liquid clouds and for improving model parameterization schemes of autoconversion of cloud water into drizzle.

scholarly journals Assessment of Regional Global Climate Model Water Vapor Bias and Trends Using Precipitable Water Vapor (PWV) Observations from a Network of Global Positioning Satellite (GPS) Receivers in the U.S. Great Plains and Midwest

2012 ◽  
Vol 25 (16) ◽  
pp. 5471-5493 ◽  
Author(s):  
Jacola A. Roman ◽  
Robert O. Knuteson ◽  
Steven A. Ackerman ◽  
David C. Tobin ◽  
Henry E. Revercomb
Keyword(s):  
Water Vapor ◽  
Great Plains ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Precipitable Water ◽  
Specific Humidity ◽  
Model Output ◽  
Global Positioning ◽  
The U.S

Abstract Precipitable water vapor (PWV) observations from the National Center of Atmospheric Research (NCAR) SuomiNet networks of ground-based global positioning system (GPS) receivers and the National Oceanic and Atmospheric Administration (NOAA) Profiler Network (NPN) are used in the regional assessment of global climate models. Study regions in the U.S. Great Plains and Midwest highlight the differences among global climate model output from the Fourth Assessment Report (AR4) Special Report on Emissions Scenarios (SRES) A2 scenario in their seasonal representation of column water vapor and the vertical distribution of moisture. In particular, the Community Climate System model, version 3 (CCSM3) is shown to exhibit a dry bias of over 30% in the summertime water vapor column, while the Goddard Institute for Space Studies Model E20 (GISS E20) agrees well with PWV observations. A detailed assessment of vertical profiles of temperature, relative humidity, and specific humidity confirm that only GISS E20 was able to represent the summertime specific humidity profile in the atmospheric boundary layer (<3%) and thus the correct total column water vapor. All models show good agreement in the winter season for the region. Regional trends using station-elevation-corrected GPS PWV data from two complimentary networks are found to be consistent with null trends predicted in the AR4 A2 scenario model output for the period 2000–09. The time to detect (TTD) a 0.05 mm yr−1 PWV trend, as predicted in the A2 scenario for the period 2000–2100, is shown to be 25–30 yr with 95% confidence in the Oklahoma–Kansas region.

scholarly journals Uncertainties of ground-based microwave radiometer retrievals in zenith and off-zenith observations under snow conditions

2017 ◽  
Vol 10 (1) ◽  
pp. 155-165 ◽  
Author(s):  
Wengang Zhang ◽  
Guirong Xu ◽  
Yuanyuan Liu ◽  
Guopao Yan ◽  
Dejun Li ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Microwave Radiometer ◽  
Retrieval Algorithm ◽  
Mean Square ◽  
Atmospheric Conditions ◽  
Vapor Density ◽  
Heavy Snowfall ◽  
Snow Conditions ◽  
The Impact ◽  
Mean Square Errors

Abstract. This paper is to investigate the uncertainties of microwave radiometer (MWR) retrievals in snow conditions and also explore the discrepancies of MWR retrievals in zenith and off-zenith observations. The MWR retrievals were averaged in a ±15 min period centered at sounding times of 00:00 and 12:00 UTC and compared with radiosonde observations (RAOBs). In general, the MWR retrievals have a better correlation with RAOB profiles in off-zenith observations than in zenith observations, and the biases (MWR observations minus RAOBs) and root mean square errors (RMSEs) between MWR and RAOB are also clearly reduced in off-zenith observations. The biases of temperature, relative humidity, and vapor density decrease from 4.6 K, 9 %, and 1.43 g m−3 in zenith observations to −0.6 K, −2 %, and 0.10 g m−3 in off-zenith observations, respectively. The discrepancies between MWR retrievals and RAOB profiles by altitude present the same situation. Cases studies show that the impact of snow on accuracies of MWR retrievals is more serious in heavy snowfall than in light snowfall, but off-zenith observation can mitigate the impact of snowfall. The MWR measurements become less accurate in snowfall mainly due to the retrieval algorithm, which does not consider the effect of snow, and the accumulated snow on the top of the radome increases the signal noise of MWR measurements. As the snowfall drops away by gravity on the sides of the radome, the off-zenith observations are more representative of the atmospheric conditions for RAOBs.

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